Using Fully Convolutional Networks for Floor Area Detection

Abstract

Most mobile robots use visual images to obtain information about the surrounding environment and the nonlinear diffusion method to detect candidate areas of the floor, but they could not be applied to more complicated environments. In this study, a hybrid of fully convolutional networks (FCNs) and fuzzy integral is proposed for detecting the position of the floor and nonfloor from visual images. FCN is an end-to-end, pixels-to-pixels network for semantic segmentation. Semantic segmentation aims to perform dense segmentation tasks on images and segments each pixel to a specified category. To overcome the majority decision drawback in the traditional voting method and increase the accuracy, the fuzzy integral is used for the fusion of multiple FCNs with various optimal methods. The overall accuracy, mean accuracy, and mean intersection over union (MIoU) of the proposed method are 0.9824, 0.9816, and 0.9577, respectively. The experimental results show that the proposed hybrid method has better accuracy than other methods in identifying the location of the floor area.

A Deep Learning Approach Towards Railway Safety Risk Assessment

“…using a random forest algorithm [122] to perform crack detection on 3D asphalt surfaces [123]. Due to their high performance and promising results, convolutional neural networks (CNNs) have been utilised in visual computing in many studies in the field [124]- [126] and for floor area detection [127]. Due to their high performance and promising results, convolutional neural has led to extreme weather, while demand causes the industry to raise capacity and increase the number of trains in the system.…”